Regression with Gaussian processes

Christopher K. I. Williams

    Research output: Chapter in Book/Published conference outputChapter


    The Bayesian analysis of neural networks is difficult because the prior over functions has a complex form, leading to implementations that either make approximations or use Monte Carlo integration techniques. In this paper I investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis to be carried out exactly using matrix operations. The method has been tested on two challenging problems and has produced excellent results.
    Original languageEnglish
    Title of host publicationMathematics of neural networks
    Subtitle of host publicationmodels, algorithms and applications
    EditorsStephen W. Ellacott, John C. Mason, Iain J. Anderson
    Number of pages5
    ISBN (Print)978-0-7923-9933-9
    Publication statusPublished - 1997

    Publication series

    NameOperations Research/Computer Science Interfaces Series
    PublisherKluwer (Now part of Springer)

    Bibliographical note

    Copyright of Kluwer(now part of Springer). The original publication is available at


    • Bayesian analysis
    • complex form
    • integration
    • Gaussian process
    • matrix operations


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